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Text Categorization

Text Categorization is the task of automatically assigning pre-defined categories to documents written in natural languages. Several types of Text Categorization have been studied, each of which deals with different types of documents and categories, such as topic categorization to detect discussed topics (e.g., sports, politics), spam detection, and sentiment classification to determine the sentiment typically in product or movie reviews.

Source: Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

Papers

Showing 101110 of 247 papers

TitleStatusHype
Finding a Character's Voice: Stylome Classification on Literary Characters0
French and German Corpora for Audience-based Text Type Classification0
From high heels to weed attics: a syntactic investigation of chick lit and literature0
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings0
FSMJ: Feature Selection with Maximum Jensen-Shannon Divergence for Text Categorization0
A Practical Perspective on Latent Structured Prediction for Coreference Resolution0
GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts0
Graph-based Semi-Supervised Learning Algorithms for NLP0
Handling Imbalanced Dataset in Multi-label Text Categorization using Bagging and Adaptive Boosting0
HeLI-based Experiments in Discriminating Between Dutch and Flemish Subtitles0
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